Salah satu fantasi masa remaja saya adalah mengendari Shelby Mustang 1966 GT 350 sendirian dari Pantai Timur sampai Pantai Barat Amerika. Musik rock digeber keras sepanjang perjalanan. Menyusuri Rute 66 yang legendaris, melewati kota-kota kecil yang hampir mati. Mampir ke klub-klub musik blues, berharap bertemu titisan Robert Johnson atau Stevie Ray Vaughn. Jalan malam menembus padang pasir atau badlands.
Seringkali ditengah kelas, alih-alih menyalin isi papan tulis, saya lebih sibuk membongkar-pasang playlist yang akan menemani "perjalanan spiritual" ini (seakan-akan berangkat minggu depan haha...). Night Ranger's (You Can Still) Rock In America, Def Leppard's Hello America, Led Zeppelin's Immigrant Song, Jimi Hendrix's Hear My Train Comin', Procol Harum's Whisky Train, Chuck Berry's Johnny B. Goode, dan David Lee Roth's Tobacoo Road termasuk dalam daftar teratas.
Karena menginjak Amerika baru kesampaian setelah dewasa dan berkeluarga, maka fantasi itu perlu dimodifikasi. Nevertheless, it still rocks too! (Disergap hujan badai di London, OH, mampir ke Cleveland's Rock 'n Roll Hall of Fame, dan ditutup di Chicago's Lollapalooza 2010 sebelum pulang ke Urbana).
Dokumentasi video perjalanan juga dapat dilihat di Youtube.
Tuesday, May 3, 2011
Sunday, May 1, 2011
My Homes
Home goes beyond a geographical concept. To me it's a state of mind, so I'm happy to call Surabaya and Champaign-Urbana as my homes.
Beatles defines home as a sanctuary from a hectic day 'cause you'll see the loved ones. Home is where you sense a peaceful and beautiful moment, right goes straight to your heart as you're listening to Vito Bratta's finest solo. Home is sweet, cause you know you won't be left all alone.
Beatles defines home as a sanctuary from a hectic day 'cause you'll see the loved ones. Home is where you sense a peaceful and beautiful moment, right goes straight to your heart as you're listening to Vito Bratta's finest solo. Home is sweet, cause you know you won't be left all alone.
The Curse of Education
Despite a long time Education for All movement, we can observe many inequalities in education or education-induced inequality. Underachievement in education is one of the most potent factors in transmitting poverty across generation. Low income groups have high drop out rates and lower progression from primary to secondary education. Differences in educational attainment, allied to wider social and cultural factors, help explain male-female income gaps in many economies.
Social scientists have long observed the "curse of education". Here is some important linkages:
Education, inequality, poverty-The educational system in many developing countries act to increase rather than decrease income inequalities because:
Social scientists have long observed the "curse of education". Here is some important linkages:
Education, inequality, poverty-The educational system in many developing countries act to increase rather than decrease income inequalities because:
- There is positive correlation between education and income.
- The poor are effectively denied access to secondary and higher education because of higher cost (mainly opportunity cost) lower benefit (more difficult to get high pay jobs in urban areas due to lack of connection). So rate of dropouts much higher for the poor.
- Family environment, peer group interaction, and early malnutrition and poor health all work to the disadvantage of poor students in learning.
- Curricula have strong urban bias and pay little attention to priority needs of majority of student who live and work in rural areas.
- The wholesale import of formal educational system from developed countries into developing countries contributes not only to external brain drain (migration to developed countries) but also to internal brain drain (diverting attention of professionals from local problems and needs).
Hardiknas 2 Mei 2011
Hari ini ber-solilokui Hymne Guru (ah, mataku membasah).
Apa kabar Wajib Belajar Indonesia?Sebagian jawaban dapat ditemukan disini:
Apa kabar Wajib Belajar Indonesia?
- Barro & Lee. Datanya kaya, bisa dipakai sekedar untuk perbandingan snapshot maupun melihat lebih jauh bagaimana kontribusi modal manusia terhadap kinerja ekonomi.
- Statistik pendidikan UNESCO maupun indikator pembangunan manusia UNDP.
Monday, April 21, 2008
Convergence or Divergence?
(This writing is an Abstract and Conclusion part of my conference paper, presented in the 8th International Conference of IRSA/Indonesian Regional Science Association and the 31st Annual Meeting and Conference of FAEA/Federation of Asian Economic Association. Both were in 2007).
Abstract
This paper studies convergence process across cities in East Java province of Indonesia. To do so, it particularly investigates the effects of human capital and public capital on convergence trend. It also explores the geographic dimension of knowledge spillovers that is perceived as a key mechanism for convergence and a particular pattern of economic (industrial) activities. This paper provides some evidence on the trend of economic divergence. During the study period, divergence trend was associated with both economic liberalisation and decentralisation, while convergence trend shortly existed during economic crisis. Further, the process of convergence is conditional. The effectiveness of policy interventions that may be able to enhance convergence is also discussed in this paper.
Main Points
This paper provides some evidence on the trend of economic divergence. During the study period, divergence trend was associated with both economic liberalisation and decentralisation, while convergence trend shortly existed during economic crisis. Further, the process of convergence is conditional. Conditional convergence is basically equivalent to sustained differences in the levels of city real output or income per capita, as evidenced by estimates with LSDV model, as supported with spatial regression model, and as implied with the pattern of concentration and specialisation. While it is true that there is some degree of (global) spatial autocorrelation, it is not strong enough to leak out some growth - enhancing spillover effects, such as knowledge spillovers. Thus, knowledge that is important to improve productivity (that eventually leads to higher growth rate) will only be contained in some clustering cities which are already rich and densely populated, providing higher degree of proximity among economic agents and facilitating knowledge externalities. Analysis on industrial concentration and cities’ specialization suggests that large geographical concentration of manufacturing employment has remained and convergence will therefore be conditional.
Is there any means to soften concentration to promote convergence? Improving infrastructure in East Java’s periphery cities is often suggested as one of policy measures. However, whether the outcome of improved infrastructure will promote spatial diffusion or, instead, enhance concentration is not clear, as there exists two conflicting forces at work. As infrastructure is improved, foreign firms which exercise a relatively large export share in comparison to domestic firms will more focus on cheap production possibilities. This behaviour will have a positive effect on spatial diffusion. Yet, even among foreign owned firms is it only in a few manufacturing sectors that the bulk of the production is exported. Thus, it seems reasonable to expect that the effect from improved infrastructure will be ambiguous and not necessarily reduce concentration (Sjöholm, (…), unpublished). In line with that notion, a variable of public capital used in this paper, utilised as a proxy for infrastructure, shows negative or non-significant relationship with convergence process (see table 2 for unconditional convergence and table 3 for conditional convergence). One interpretation can be derived from these results is that the spending of public capital (on infrastructure) is not cost effective, suffering from poor design and planning, mark-up, and corruption.
Though the role of human capital in this study proved to be convergence enhancing, I think that politicians and public officials tend to prefer infrastructure to human capital projects, as the former more fits their political or financial interests. As development strategy, from political perspective, infrastructure projects better fit five years of political cycle. Infrastructure projects take shorter time and are more visible, which are politically useful for boosting the chance of being (re)elected. Thus, more balanced development strategy between infrastructure and human capital projects is certainly a sensible policy option to reduce concentration and to promote convergence.
Under regional autonomy regime at city level that has been just implemented in Indonesia for about four years, city governments in East Java province are likely tempted to pursue some sorts of territorial competition policyto affect firms’ location decisions, while at the same time, less efforts are devoted in developing other local development strategies. Imbalanced economic performance will be augmented with imbalanced local development strategy by each city governments. Both literature and empirical works warn that such a policy option may be unnecessary or, worse, act as a strategy of waste (e.g. Cheshire and Gordon, 1996; Cheshire and Gordon, 1998; Rodríguez-Pose and Arbix, 2001). Considering that East Java province has long been one of the Indonesia’s main destinations of manufacturing activities (Mackie, 1993) and the spatial patterns as observed in this study, such a territorial competition policy, especially the poorly designed one, may be unnecessary for ‘core’ cities along Surabaya-Malang corridor. As a worse case, it may bring financial and technical burdens for ‘periphery’ cities. Thus, for city governments, this study should shed a light that a policy of ‘doing nothing’ with regard to East Java industrial agglomeration economies should be regarded as, if not the best policy, one of the sensible policy alternatives.
Some places benefit from structures created in the past, through a cumulative process, which has led to agglomeration economies. Hence, a pessimistic opinion about the relevance of policies attempting to reduce disparities between locations is a consequence of the analysis. If history plays such an important role, concentration of economic activity in places where agglomeration has been a product of a long time period seems inevitable. Furthermore, does there exist a cumulative process through history, policies cannot affect much density and thus productivity (Irawan and Allegria, 2006).
However, it does not mean that a policy of ‘doing something’ to promote human capital, local firms, and business environment are not encouraged. Discussing the paper by Ciccone and Hall (1996), Irawan and Allegria (2006) suggest that attempts of local and regional governments in order to increase productivity should be conducted to increase density. If concentration and proximity of workers is important, development of employment clusters or improvements of the city business centre are relevant to obtain productivity gains. In addition, policies satisfying the demand as well as reducing the price of commuting and the costs of doing business should contribute to the recreation of employment, reinforcing business and workers concentration, and agglomeration economies. At this point, the differences of underlying condition for convergence can be gradually reduced. Therefore, ‘the membership’ of convergence club can be expected to slowly expand.
Abstract
This paper studies convergence process across cities in East Java province of Indonesia. To do so, it particularly investigates the effects of human capital and public capital on convergence trend. It also explores the geographic dimension of knowledge spillovers that is perceived as a key mechanism for convergence and a particular pattern of economic (industrial) activities. This paper provides some evidence on the trend of economic divergence. During the study period, divergence trend was associated with both economic liberalisation and decentralisation, while convergence trend shortly existed during economic crisis. Further, the process of convergence is conditional. The effectiveness of policy interventions that may be able to enhance convergence is also discussed in this paper.
Main Points
This paper provides some evidence on the trend of economic divergence. During the study period, divergence trend was associated with both economic liberalisation and decentralisation, while convergence trend shortly existed during economic crisis. Further, the process of convergence is conditional. Conditional convergence is basically equivalent to sustained differences in the levels of city real output or income per capita, as evidenced by estimates with LSDV model, as supported with spatial regression model, and as implied with the pattern of concentration and specialisation. While it is true that there is some degree of (global) spatial autocorrelation, it is not strong enough to leak out some growth - enhancing spillover effects, such as knowledge spillovers. Thus, knowledge that is important to improve productivity (that eventually leads to higher growth rate) will only be contained in some clustering cities which are already rich and densely populated, providing higher degree of proximity among economic agents and facilitating knowledge externalities. Analysis on industrial concentration and cities’ specialization suggests that large geographical concentration of manufacturing employment has remained and convergence will therefore be conditional.
Is there any means to soften concentration to promote convergence? Improving infrastructure in East Java’s periphery cities is often suggested as one of policy measures. However, whether the outcome of improved infrastructure will promote spatial diffusion or, instead, enhance concentration is not clear, as there exists two conflicting forces at work. As infrastructure is improved, foreign firms which exercise a relatively large export share in comparison to domestic firms will more focus on cheap production possibilities. This behaviour will have a positive effect on spatial diffusion. Yet, even among foreign owned firms is it only in a few manufacturing sectors that the bulk of the production is exported. Thus, it seems reasonable to expect that the effect from improved infrastructure will be ambiguous and not necessarily reduce concentration (Sjöholm, (…), unpublished). In line with that notion, a variable of public capital used in this paper, utilised as a proxy for infrastructure, shows negative or non-significant relationship with convergence process (see table 2 for unconditional convergence and table 3 for conditional convergence). One interpretation can be derived from these results is that the spending of public capital (on infrastructure) is not cost effective, suffering from poor design and planning, mark-up, and corruption.
Though the role of human capital in this study proved to be convergence enhancing, I think that politicians and public officials tend to prefer infrastructure to human capital projects, as the former more fits their political or financial interests. As development strategy, from political perspective, infrastructure projects better fit five years of political cycle. Infrastructure projects take shorter time and are more visible, which are politically useful for boosting the chance of being (re)elected. Thus, more balanced development strategy between infrastructure and human capital projects is certainly a sensible policy option to reduce concentration and to promote convergence.
Under regional autonomy regime at city level that has been just implemented in Indonesia for about four years, city governments in East Java province are likely tempted to pursue some sorts of territorial competition policyto affect firms’ location decisions, while at the same time, less efforts are devoted in developing other local development strategies. Imbalanced economic performance will be augmented with imbalanced local development strategy by each city governments. Both literature and empirical works warn that such a policy option may be unnecessary or, worse, act as a strategy of waste (e.g. Cheshire and Gordon, 1996; Cheshire and Gordon, 1998; Rodríguez-Pose and Arbix, 2001). Considering that East Java province has long been one of the Indonesia’s main destinations of manufacturing activities (Mackie, 1993) and the spatial patterns as observed in this study, such a territorial competition policy, especially the poorly designed one, may be unnecessary for ‘core’ cities along Surabaya-Malang corridor. As a worse case, it may bring financial and technical burdens for ‘periphery’ cities. Thus, for city governments, this study should shed a light that a policy of ‘doing nothing’ with regard to East Java industrial agglomeration economies should be regarded as, if not the best policy, one of the sensible policy alternatives.
Some places benefit from structures created in the past, through a cumulative process, which has led to agglomeration economies. Hence, a pessimistic opinion about the relevance of policies attempting to reduce disparities between locations is a consequence of the analysis. If history plays such an important role, concentration of economic activity in places where agglomeration has been a product of a long time period seems inevitable. Furthermore, does there exist a cumulative process through history, policies cannot affect much density and thus productivity (Irawan and Allegria, 2006).
However, it does not mean that a policy of ‘doing something’ to promote human capital, local firms, and business environment are not encouraged. Discussing the paper by Ciccone and Hall (1996), Irawan and Allegria (2006) suggest that attempts of local and regional governments in order to increase productivity should be conducted to increase density. If concentration and proximity of workers is important, development of employment clusters or improvements of the city business centre are relevant to obtain productivity gains. In addition, policies satisfying the demand as well as reducing the price of commuting and the costs of doing business should contribute to the recreation of employment, reinforcing business and workers concentration, and agglomeration economies. At this point, the differences of underlying condition for convergence can be gradually reduced. Therefore, ‘the membership’ of convergence club can be expected to slowly expand.
Crime and the City
(The following is an excerpt from my conference paper, Crime and the City: An Economic Model and Evidence, presented at International Conference on “Improving the quality of human life: multidisciplinary approach on strategic relevance for urban issues”
September 6-7,2007, JW Marriot, Surabaya, Indonesia).
ABSTRACT: Using the Metropolitan Police Force’s data on London crime, this paper aims to assess whether the observed spatial concentration of crime is due to underlying people-place characteristics or to spatial spillovers. To explain spatial variations of urban crime, this paper employs some techniques of spatial econometrics and put forward two perspectives, namely: economic model and social interactions. This paper finds evidence of spatial spillovers of crime between neighbouring areas and argues that the channels for such spillovers are peer effects between single and less educated people. The peer effects are found to be larger for economically motivated crime.
The first murderer (Cain) built the first city
(Genesis 10:6, as in Glaeser and Sacerdote (1999: S243))
1 INTRODUCTION
It has long been observed that big cities have higher crime rates than in small cities or rural areas. Glaeser and Sacerdote (1999) find the positive association between city size (measured as natural log of city population) and crime rates per capita as well as murders per capita in the U.S., while Gibbons (2004) presents a cross-countries comparison, showing that crime is positively correlated with the countries’ degree of urbanisation. On the whole, there is a general pattern that crime tends to be geographically concentrated in big cities, metropolitan areas, or highly urbanised areas.
In addition, it is also identified that there is a spatial concentration of crime within cities. In other words, there is unevenness in crime distribution within cities or there are indeed ‘hot spots’ of crime within cities. As cited in Zenou (2003), Grogger and Willis (2000) point out that central cities are more prone to most crimes than suburbs. It is documented that, in U.S. between 1985 and 1992, central cities’ average crime victimisation rates were 0.409 per household, while they were much lower in suburbs, i.e. 0.306 per household (Bearse, 1996 in Zenou, 2003). Gibbons (2004) shows that in Greater London, there are 44.3 burglaries per 1000 households in Hackney, while the rates in Sutton are 12 burglaries per 1000 households. In New York City (NYC), the rates of serious crimes per capita vary, from 0.022 serious crimes per capita in the 123rd precinct of NCY to 0.21 serious crimes per capita in the 1st precinct of NCY (Glaeser et al., 1996).
A simple explanation suggests that these stylised facts are due to the variances of economic and social conditions across areas within cities. The study by Hakim (1980) finds that property crimes have a direct relation with an area’s relative wealth, while Costello and Wiles (2001: 31) review some researches that generally find that “offenders offend in areas dominated by the same ethnic group as the offender”. In this regard, in a single city, we can expect that personal assault crimes will be much higher in certain areas than the others.
However, more recent study by Glaeser et al. (1996) finds that these demographic and social variables are only able to account for little (no more than 30%) of the variation in crime across areas in NYC. This finding is in line with the ‘neighbourhood effects’ or ‘peer interactions’ story in explaining the spatial patterns of crime within cities. This theory argues that it is not only economic incentives that determine individual behaviour, but it may also depend on the behaviour of peers or neighbours (Freeman et al., 1996). In other words, provided that committing crime may give the same expected returns, individuals may be prone to crime, if their peers commit crime than they do otherwise (Freeman, 1999). Therefore, social interaction models should be viewed more complementary than substitute to economic models.
With regard to such phenomena, this paper is aiming at investigating the spatial pattern of crime across London wards, based on the Metropolitan Police Force’s data and the 2001 U.K. Census Data. In order to be able to provide explanation on what might constitute the crime disparities in London, this paper develops models that incorporate both wards’ individual characteristics and spatial spillover effects. The computed value of Moran’s I suggests that the spatial autocorrelation in crime among London wards does exist, hence, following Anselin (1988), spatial lag variables should be taken into account.
To summarise the results of this paper briefly: spatial spillovers do account for sizeable differences in crime clusters across London wards and improve explanatory power of the model that is otherwise only explained by underlying population characteristics alone. This paper starts by reviewing related literature on the determinants of crime in Section 2, followed by empirical framework in Section 3. Section 4 presents and discusses the descriptive and estimation results, while Section 5 concludes and provides some policy implications.
September 6-7,2007, JW Marriot, Surabaya, Indonesia).
ABSTRACT: Using the Metropolitan Police Force’s data on London crime, this paper aims to assess whether the observed spatial concentration of crime is due to underlying people-place characteristics or to spatial spillovers. To explain spatial variations of urban crime, this paper employs some techniques of spatial econometrics and put forward two perspectives, namely: economic model and social interactions. This paper finds evidence of spatial spillovers of crime between neighbouring areas and argues that the channels for such spillovers are peer effects between single and less educated people. The peer effects are found to be larger for economically motivated crime.
The first murderer (Cain) built the first city
(Genesis 10:6, as in Glaeser and Sacerdote (1999: S243))
1 INTRODUCTION
It has long been observed that big cities have higher crime rates than in small cities or rural areas. Glaeser and Sacerdote (1999) find the positive association between city size (measured as natural log of city population) and crime rates per capita as well as murders per capita in the U.S., while Gibbons (2004) presents a cross-countries comparison, showing that crime is positively correlated with the countries’ degree of urbanisation. On the whole, there is a general pattern that crime tends to be geographically concentrated in big cities, metropolitan areas, or highly urbanised areas.
In addition, it is also identified that there is a spatial concentration of crime within cities. In other words, there is unevenness in crime distribution within cities or there are indeed ‘hot spots’ of crime within cities. As cited in Zenou (2003), Grogger and Willis (2000) point out that central cities are more prone to most crimes than suburbs. It is documented that, in U.S. between 1985 and 1992, central cities’ average crime victimisation rates were 0.409 per household, while they were much lower in suburbs, i.e. 0.306 per household (Bearse, 1996 in Zenou, 2003). Gibbons (2004) shows that in Greater London, there are 44.3 burglaries per 1000 households in Hackney, while the rates in Sutton are 12 burglaries per 1000 households. In New York City (NYC), the rates of serious crimes per capita vary, from 0.022 serious crimes per capita in the 123rd precinct of NCY to 0.21 serious crimes per capita in the 1st precinct of NCY (Glaeser et al., 1996).
A simple explanation suggests that these stylised facts are due to the variances of economic and social conditions across areas within cities. The study by Hakim (1980) finds that property crimes have a direct relation with an area’s relative wealth, while Costello and Wiles (2001: 31) review some researches that generally find that “offenders offend in areas dominated by the same ethnic group as the offender”. In this regard, in a single city, we can expect that personal assault crimes will be much higher in certain areas than the others.
However, more recent study by Glaeser et al. (1996) finds that these demographic and social variables are only able to account for little (no more than 30%) of the variation in crime across areas in NYC. This finding is in line with the ‘neighbourhood effects’ or ‘peer interactions’ story in explaining the spatial patterns of crime within cities. This theory argues that it is not only economic incentives that determine individual behaviour, but it may also depend on the behaviour of peers or neighbours (Freeman et al., 1996). In other words, provided that committing crime may give the same expected returns, individuals may be prone to crime, if their peers commit crime than they do otherwise (Freeman, 1999). Therefore, social interaction models should be viewed more complementary than substitute to economic models.
With regard to such phenomena, this paper is aiming at investigating the spatial pattern of crime across London wards, based on the Metropolitan Police Force’s data and the 2001 U.K. Census Data. In order to be able to provide explanation on what might constitute the crime disparities in London, this paper develops models that incorporate both wards’ individual characteristics and spatial spillover effects. The computed value of Moran’s I suggests that the spatial autocorrelation in crime among London wards does exist, hence, following Anselin (1988), spatial lag variables should be taken into account.
To summarise the results of this paper briefly: spatial spillovers do account for sizeable differences in crime clusters across London wards and improve explanatory power of the model that is otherwise only explained by underlying population characteristics alone. This paper starts by reviewing related literature on the determinants of crime in Section 2, followed by empirical framework in Section 3. Section 4 presents and discusses the descriptive and estimation results, while Section 5 concludes and provides some policy implications.
A Market for Paid Sex
What will economists say about a market for paid sex?. Steven Levitt (Chicago’s economist, co-author of best selling book Freakonomics) and Sudhir Venkatesh (Columbia’s sociologist) did empirical analysis on street-level prostitution. Their research on the economics of street level prostitution uses official arrests record and field data on 2,200 transactions, collected from sex workers in three Chicago districts. Their work’s preliminary finding was presented in the Annual Meeting of American Economic Association on January 2008.
Here are main findings:
Prostitution, as a market, is highly concentrated in few locations. Yes, because prostitutes and their clients need to find each other easily. The market is large too. They estimate there are 4,400 street prostitutes active in Chicago in an average week.
Prostitutes earn premium wages, about $25-$30 per hour. It is about four times higher than they can earn in other productive activities. Yet, taking into account job risks, this amount is relatively meager compensation. The norm of sex without a condom and regular violent assault at least once a month is among the risks. Despite, the risk of legal action is low, opportunity costs can be high, since prostitutes are more likely to have sex (without get paid) with a police officer than to be arrested by one.
Prostitutes are also practicing pricing strategy. They maximize returns by segmenting market and assessing client’s perceived ability and willingness to pay. White customers are generally charged higher than black ones. There is a paradox. Though price increases along higher risks, but price premium for unprotected sex is small.
Supply of prostitutes is relatively elastic in a location where more people gathered for a national occasion. The 30% price hike could boost supply by 60%. Supply was flexible because regular prostitutes worked for more hours and prostitutes in other locations came in plus some seasonal prostitutes who are willing to offer sex service for higher wages.
Here are main findings:
Prostitution, as a market, is highly concentrated in few locations. Yes, because prostitutes and their clients need to find each other easily. The market is large too. They estimate there are 4,400 street prostitutes active in Chicago in an average week.
Prostitutes earn premium wages, about $25-$30 per hour. It is about four times higher than they can earn in other productive activities. Yet, taking into account job risks, this amount is relatively meager compensation. The norm of sex without a condom and regular violent assault at least once a month is among the risks. Despite, the risk of legal action is low, opportunity costs can be high, since prostitutes are more likely to have sex (without get paid) with a police officer than to be arrested by one.
Prostitutes are also practicing pricing strategy. They maximize returns by segmenting market and assessing client’s perceived ability and willingness to pay. White customers are generally charged higher than black ones. There is a paradox. Though price increases along higher risks, but price premium for unprotected sex is small.
Supply of prostitutes is relatively elastic in a location where more people gathered for a national occasion. The 30% price hike could boost supply by 60%. Supply was flexible because regular prostitutes worked for more hours and prostitutes in other locations came in plus some seasonal prostitutes who are willing to offer sex service for higher wages.
Subscribe to:
Posts (Atom)
